Search Results (5,300)

The implementation of Building Information Modelling (BIM) in public design processes enhances efficiency, transparency, and sustainability. However, public agencies often encounter significant barriers, particularly regarding organisational and managerial readiness. This study develops a BIM implementation framework tailored to the specific needs of an Italian public agency. The research adopts a qualitative approach, combining 15 semi-structured interviews with process mapping Using (Business Process Modeling Notation) BPMN. The current as-is workflows were analysed and validated by internal stakeholders. Based on this analysis, strategic objectives were defined, relevant (Building Information Modelling) BIM uses were selected, and revised to-be processes were proposed, integrating new roles and responsibilities according to the standards. The framework addresses both technical and organisational dimensions of BIM adoption, highlighting the need for training, coordination, and stakeholder engagement. The main outcomes include a structured process model, a priority-based selection of BIM uses, and a role matrix supporting organisational transformation. The added value for researchers lies in the replicable methodology that combines empirical process mapping with implementation planning. For practitioners, especially consultants in sustainable design, the study offers a practical roadmap for aligning BIM adoption with project goals, regulatory compliance, and environmental performance targets in complex public sector contexts. Full article

Electrostatic separation is a promising technology for the recovery of valuable metals from electronic waste, particularly from printed circuit boards (PCBs). This study explores the application of electrostatic separation for the selective recovery of metallic and non-metallic fractions from crushed PCBs (PCBs). The process exploits the differences in electrical properties between conductive metals and non-conductive polymers and ceramics, facilitating their separation through applied electric fields. The raw materials were pre-treated via mechanical comminution using shredders and hammer mills to achieve an optimal particle size distribution (<3 mm), which enhances separation efficiency. Ferrous materials were removed prior to electrostatic separation to improve process selectivity. Key operational parameters, including particle size, charge accumulation, environmental conditions, and separation efficiency, were systematically analysed. The results demonstrate that electrostatic separation effectively recovers high-value metals such as copper and gold while minimizing material losses. Additionally, the process contributes to the sustainability of e-waste recycling by enabling the recovery of non-metallic fractions for potential secondary applications. This work underscores the significance of electrostatic separation as a viable technique for e-waste management and highlights optimization strategies for enhancing its performance in large-scale recycling operations. Full article

Changes in habitat suitability are critical indicators of the ecological impacts of climate change. Syndiclis Hook. f., a rare and endangered genus endemic to montane limestone and cloud forest ecosystems in China, holds considerable ecological and economic value. However, knowledge of its current distribution and the key environmental factors influencing its habitat suitability remains limited. In this study, we employed the MaxEnt model, integrated with geographic information systems (ArcGIS), to predict the potential distribution of Syndiclis under current and future climate scenarios, identify dominant bioclimatic drivers, and assess temporal and spatial shifts in habitat patterns. We also analyzed spatial displacement of habitat centroids to explore potential migration pathways. The model demonstrated excellent performance (AUC = 0.988), with current suitable habitats primarily located in Hainan, Taiwan, Southeastern Yunnan, and along the Yunnan–Guangxi border. Temperature seasonality (bio7) emerged as the most important predictor (67.00%), followed by precipitation of the driest quarter (bio17, 14.90%), while soil factors played a relatively minor role. Under future climate projections, Hainan and Taiwan are expected to serve as stable climatic refugia, whereas the overall suitable habitat area is projected to decline significantly. Combined with topographic constraints, population decline, and limited dispersal ability, these changes elevate the risk of extinction for Syndiclis in the wild. Landscape pattern analysis revealed increased habitat fragmentation under warming conditions, with only 4.08% of suitable areas currently under effective protection. We recommend prioritizing conservation efforts in regions with habitat contraction (e.g., Guangxi and Yunnan) and stable refugia (e.g., Hainan and Taiwan). Conservation strategies should integrate targeted in situ and ex situ actions, guided by dominant environmental variables and projected migration routes, to ensure the long-term persistence of Syndiclis populations and support evidence-based conservation planning. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

The discharge of synthetic dyes in industrial wastewaters poses a serious environmental threat as they are difficult to degrade naturally and are harmful to aquatic organisms. This study aimed to evaluate the feasibility of using clean untreated rice husk (CRH) as a sustainable and low-cost adsorbent for the removal of methylene blue (MB) from synthetic wastewater. This approach effectively avoids the energy-intensive grinding process by directly using whole unprocessed rice husk, highlighting its potential as a sustainable and cost-effective alternative to activated carbon. A series of batch adsorption experiments were conducted to evaluate the effects of key operating parameters such as initial dye concentration, contact time, pH, ionic strength, and temperature on the adsorption performance. Adsorption kinetics, isotherm models, and thermodynamic analysis were applied to elucidate the adsorption mechanism and behavior. The results showed that the maximum adsorption capacity of CRH for MB was 5.72 mg/g. The adsorption capacity was stable and efficient between pH 4 and 10, and reached the highest value at pH 12. The presence of sodium ions (Na⁺) and calcium ions (Ca²⁺) inhibited the adsorption efficiency, with calcium ions having a more significant effect. Kinetic analysis confirmed that the adsorption process mainly followed a pseudo-second-order model, suggesting the involvement of a chemisorption mechanism; notably, in the presence of ions, the Elovich model provided better predictions of the data. Thermodynamic evaluation showed that the adsorption was endothermic (ΔH° > 0) and spontaneous (ΔG° < 0), accompanied by an increase in the disorder of the solid–liquid interface (ΔS° > 0). The calculated activation energy (Ea) was 17.42 kJ/mol, further supporting the involvement of chemisorption. The equilibrium adsorption data were well matched to the Langmuir model at high concentrations (monolayer adsorption), while they were accurately described by the Freundlich model at lower concentrations (surface heterogeneity). The dimensionless separation factor (RL) confirmed that the adsorption process was favorable at all initial MB concentrations. The results of this study provide insights into the application of agricultural waste in environmental remediation and highlight the potential of untreated whole rice husk as a sustainable and economically viable alternative to activated carbon, which can help promote resource recovery and pollution control. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

The efficiency of solar water heating systems is strongly influenced by variable weather conditions, making the optimization of control strategies essential for maximizing energy performance. This study presents the development and evaluation of a rule-based adaptive control strategy that dynamically selects one of three predefined control modes—ON–OFF, proportional, or indirect proportional control (IPC)—based on real-time weather classification. The classification algorithm assigns each day to one of four solar irradiance categories, enabling the controller to respond appropriately to current environmental conditions. The proposed adaptive controller was implemented and tested under real operating conditions and compared with a conventional commercial solar controller. Over a 40-day testing period, the adaptive system achieved a 12.7% increase in thermal energy storage efficiency. Specifically, despite receiving 4.8% less solar radiation (719 kWh vs. 755 kWh), the adaptive controller stored 453 kWh of heat in the water tank compared to 416 kWh with the traditional system. This corresponds to an efficiency improvement from 0.55 to 0.63. These results demonstrate the adaptive controller’s superior ability to utilize available solar energy across all weather scenarios. The findings confirm that intelligent control strategies not only enhance technical performance but also improve the economic and environmental value of solar thermal systems. Full article

The aerospace industry is increasingly turning to composite materials due to their exceptional strength, stiffness, and beneficial physical properties. However, increased reliance on carbon fibre composites has substantial environmental implications, particularly concerning waste management. Recycling these materials is a potential solution to these sustainability issues, provided the recycled fibres retain adequate mechanical strength and durability. This study evaluates the mechanical capabilities of recycled carbon fibres in a scaled-down aircraft spar model (AMT-600 GURI), contrasting them with the capabilities of conventional spars. The primary objective is to ascertain whether recycled composites can fulfil the stringent structural requirements of aerospace applications, employing both simulation and experimental validation methods. The recycled carbon fibre composites were manufactured using hand lay-up and vacuum bagging techniques, and their properties were validated through rigorous tensile and compressive strength testing. These validated results were then used to inform a finite element model developed in HyperWorks software. Simulations revealed that the recycled spar achieved maximum stress values of 3.87 MPa under lift forces, a slight increase of +8.95% compared to the original spar, and 55.05 MPa under drag forces, a significant improvement of +36%. Aerodynamic evaluations further confirmed the structural resilience of the recycled spar, with displacement measurements of 141.4 mm for lift and 504.8 mm for drag, closely aligning with the original spar’s performance. In summary, this study demonstrates that recycled carbon fibre composites can serve as effective substitutes for traditional aerospace materials, thereby supporting sustainability initiatives without compromising performance. The outlined approach provides a reliable framework for incorporating recycled materials. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

As environmental sustainability becomes a strategic priority for the hospitality sector, firms are increasingly adopting internal green marketing (IGM) practices to drive innovation. This study investigates how IGM influences innovative performance (IP) among hotel employees, focusing on the mediating roles of pro-environmental behavior (PEB) and internal green values (IGV). Drawing on data from 400 hotel employees in Egypt and analyzed using partial least squares structural equation modeling (PLS-SEM), the results reveal that while IGM significantly enhances PEB and IGV, it does not directly improve innovative performance. Instead, IGV and PEB fully mediate the relationship between IGM and IP, highlighting that innovation emerges primarily through value-driven behavior and organizational culture. These findings contribute to the sustainability and innovation literature by proposing a validated model that explains how internal marketing mechanisms foster eco-innovation. The study offers practical implications for hotel managers aiming to cultivate a sustainability-oriented culture and embed green values into daily operations to support long-term innovation. Full article

Heritage building conservation plays a special role in addressing modern sustainability challenges by preserving the cultural identity, retrofitting, restoring, and renovating these structures to improve energy performance, which is crucial for revitalisation. This research aims to use Heritage Building Information Modelling (HBIM) to increase energy efficiency and environmental sustainability in historic buildings. Retrofitting heritage buildings presents unique challenges and opportunities to simultaneously reduce energy consumption and carbon emissions while maintaining historical integrity. Traditional approaches are often insufficient to meet heritage structures’ energy needs. Modern technologies such as information building modelling and energy simulations can offer solutions. HBIM is a vigorous digital framework that facilitates interdisciplinary collaboration and offers detailed insights into building restoration and energy modelling. HBIM supports the integration of thermal and energy efficiency measures while maintaining the authenticity of heritage architecture by creating a comprehensive database. Using a case study heritage building, this research demonstrates how retrofitting the different aspects of heritage buildings can improve energy performance. Evaluating the preservation of heritage buildings’ cultural and architectural values and the effectiveness of using HBIM to model energy performance offers a viable framework for sustainable retrofitting of heritage buildings. Full article

The screening of poplar varieties that demonstrate tolerance to low nitrogen (N) represents a promising strategy for improving nitrogen-use efficiency in trees. Such an approach could reduce reliance on N fertilizers while mitigating environmental pollution associated with their cultivation. In this study, a total of 87 poplar varieties were evaluated in a controlled greenhouse pot experiment. Under both low-nitrogen (LN) and normal-nitrogen (NN) conditions, 18 traits spanning four categories—growth performance, leaf morphology, chlorophyll fluorescence, and N isotope parameters were measured. For 13 of these traits (growth, leaf morphology, chlorophyll fluorescence), genetic variation and parameters, including genotypic values, were analyzed using best linear unbiased prediction (BLUP) within a linear mixed model (LMM). LN tolerance of tested poplar varieties was comprehensively assessed with three MGIDI strategies by integrating means, BLUPs, and low-nitrogen tolerance coefficient (LNindex) to rank poplar varieties. The results exhibited highly significant differences across all traits between LN and NN experiments, as well as among varieties. LN stress markedly inhibited growth, altered leaf morphology, and reduced chlorophyll fluorescence parameters in young poplar plants. Among the selection strategies, the MGIDI_LNindex approach demonstrated the highest selection differential percent (SD% = 10.5–35.23%). Using a selection intensity (SI) of 20%, we systematically identified 17 superior genotypes across all three strategies. In a thorough, comprehensive MGIDI-based evaluation, these varieties exhibited exceptional adaptability and stability under LN stress. The selected genotypes represent valuable genetic resources for developing improved poplar cultivars with enhanced low-nitrogen tolerance. Full article

Polycarboxylate superplasticizer (PCE) is an important part of improving the overall performance of concrete. However, its synthetic raw materials are overly dependent on petrochemical products, and it also causes problems such as environmental pollution. With the development of the building material industry, the demand for petrochemical resources required for synthetic water-reducing agents will increase rapidly. Therefore, there is an urgent need to transition the synthetic raw materials of PCE from petrochemicals to biomass materials to reduce the consumption of nonrenewable resources as well as the burden on the environment. Biomass materials are inexpensive, readily available and renewable. Utilizing biomass resources to develop good-performing water-reducing agents can reduce the consumption of fossil resources. This is conducive to carbon emission reduction in the concrete material industry. In addition, it promotes the high-value utilization of biomass resources. Therefore, in this study, a biomass polyether monomer, acryloyl hydroxyethyl cellulose (AHEC), was synthesized from cellulose via the reaction route of ethylene oxide (EO) etherification and acrylic acid (AA) esterification. Biomass polycarboxylate superplasticizers (PCE-Cs) were synthesized through free radical polymerization by substituting AHEC for a portion of the frequently utilized polyether monomer isopentenyl polyoxyethylene ether (TPEG). This study primarily focused on the properties of PCE-Cs in relation to cement. The findings of this study indicated that the synthesized PCE-C5 at a dosing of 0.4% (expressed as mass fraction of cement) when the AHEC substitution ratio was 5% achieved good water reduction properties and significant delays. With the same fluidity, PCE-C5 could enhance the mechanical strength of cement mortar by 30% to 40%. This study utilized green and low-carbon biomass resources to develop synthetic raw materials for water-reducing agents, which exhibited effective water-reducing performance and enhanced the utilization rate of biomass resources, demonstrating significant application value. Full article

The confluence of financial constraints, climate change mitigation efforts, and evolving user expectations has significantly transformed the concept of facility management (FM). Traditional FM has now evolved to enhance sustainability in the built environment. Sustainable facility management (SFM) can add value to companies, organizations, and governments by balancing the financial, environmental, and social outcomes of the FM processes. The systematic literature review revealed a limited number of studies developing a performance measurement framework for SFM in office buildings and/or other building types in the literature. Given that the lack of this theoretical basis inhibits the effective deployment of SFM practices, this study aims to fill this gap by developing a performance measurement framework for SFM in office buildings. Accordingly, an in-depth literature review was initially conducted to synthesize sustainable performance measurement factors. Next, a series of focus group discussion (FGD) sessions were organized to refine and verify the factors and develop a novel performance measurement framework for SFM. Lastly, consistency analysis, the Bayesian best worst method (BBWM), and sensitivity analysis were implemented to determine the priorities of the factors. What the proposed framework introduces is the combined use of two performance measurement mechanisms, such as continuous performance measurement and comprehensive performance measurement. The continuous performance measurement is conducted using high-priority factors. On the other hand, the comprehensive performance measurement is conducted with all the factors proposed in this study. Also, the BBWM results showed that “Energy-efficient material usage”, “Percentage of energy generated from renewable energy resources to total energy consumption”, and “Promoting hybrid or remote work conditions” are the top three factors, with scores of 0.0741, 0.0598, and 0.0555, respectively. Moreover, experts should also pay the utmost attention to factors related to waste management, indoor air quality, thermal comfort, and H&S measures. In addition to its theoretical contributions, the paper makes practical contributions by enabling decision makers to measure the SFM performance of office buildings and test the outcomes of their managerial processes in terms of performance. Full article